Proyecto Fibrosis
2024-04-02
library(kableExtra)
library(ggplot2)
library(dplyr)##
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## order_bylibrary(knitr)
library(kableExtra)datos <- read.csv2("Base_datos_Fibrosis.csv")dato_por_genero <- datos %>%
group_by(genero) %>%
count() %>%
ungroup() %>%
mutate(Porcentaje = n / sum(n)) %>%
arrange(Porcentaje) %>%
mutate(etiquetas = scales::percent(Porcentaje))
colors <- c("lightgreen", "#DEB7D9")
ggplot(dato_por_genero, aes(x = "", y = Porcentaje, fill = genero)) +
geom_col(color = "black") +
geom_label(aes(label = etiquetas),
position = position_stack(vjust = 0.5),
show.legend = FALSE) +
scale_fill_manual(values = colors) +
guides(fill = guide_legend(title = "Cantidad de sujetos según género")) +
coord_polar(theta = "y") +
ggtitle("")
print(dato_por_genero)## # A tibble: 2 × 4
## genero n Porcentaje etiquetas
## <chr> <int> <dbl> <chr>
## 1 Mujer 150 0.464 46.4%
## 2 Hombre 173 0.536 53.6%# Datos numéricos
datos_numericos <- datos[, c("Glucemia", "Urea", "Creatinina", "Colesterol", "Triglicéridos",
"GOT", "GPT", "GGT", "Fosfatasa.Alcalina", "bilirrubina.total",
"proteínas.totales", "albúmina", "sodio", "potasio",
"leucocitos", "hematocrito", "plaquetas", "Índice.de.Quick",
"fibrinógeno")]
# Resumen estadístico
kable(summary(datos_numericos), caption = "Resumen estadístico de datos numéricos")| Glucemia | Urea | Creatinina | Colesterol | Triglicéridos | GOT | GPT | GGT | Fosfatasa.Alcalina | bilirrubina.total | proteínas.totales | albúmina | sodio | potasio | leucocitos | hematocrito | plaquetas | Índice.de.Quick | fibrinógeno | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 14.0 | Min. : 11.00 | Min. :0.400 | Min. : 20.0 | Min. : 7.5 | Min. : 13.00 | Min. : 16.0 | Min. : 12.0 | Min. : 27.0 | Min. :0.230 | Min. :4.600 | Min. :2.600 | Min. :128.0 | Min. :3.400 | Min. : 1700 | Min. :24.00 | Min. : 10000 | Min. : 45.00 | Min. :123.0 | |
| 1st Qu.: 88.0 | 1st Qu.: 23.50 | 1st Qu.:0.900 | 1st Qu.:158.5 | 1st Qu.:104.0 | 1st Qu.: 36.50 | 1st Qu.: 50.0 | 1st Qu.: 35.0 | 1st Qu.: 71.0 | 1st Qu.:0.700 | 1st Qu.:6.800 | 1st Qu.:4.000 | 1st Qu.:138.0 | 1st Qu.:4.200 | 1st Qu.: 4015 | 1st Qu.:38.00 | 1st Qu.:108000 | 1st Qu.: 96.00 | 1st Qu.:227.0 | |
| Median :101.0 | Median : 31.00 | Median :1.100 | Median :180.0 | Median :136.0 | Median : 56.00 | Median : 82.0 | Median : 67.0 | Median : 95.0 | Median :1.000 | Median :7.100 | Median :4.300 | Median :140.0 | Median :4.500 | Median : 5450 | Median :41.00 | Median :139000 | Median :100.00 | Median :270.5 | |
| Mean :112.2 | Mean : 35.16 | Mean :1.185 | Mean :181.6 | Mean :158.3 | Mean : 88.52 | Mean :118.8 | Mean : 116.3 | Mean :105.6 | Mean :1.148 | Mean :7.136 | Mean :4.236 | Mean :139.7 | Mean :4.536 | Mean : 5571 | Mean :41.28 | Mean :141053 | Mean : 96.01 | Mean :275.7 | |
| 3rd Qu.:122.5 | 3rd Qu.: 43.00 | 3rd Qu.:1.390 | 3rd Qu.:204.0 | 3rd Qu.:190.0 | 3rd Qu.:107.50 | 3rd Qu.:138.0 | 3rd Qu.: 134.5 | 3rd Qu.:122.5 | 3rd Qu.:1.300 | 3rd Qu.:7.500 | 3rd Qu.:4.500 | 3rd Qu.:141.0 | 3rd Qu.:4.800 | 3rd Qu.: 6770 | 3rd Qu.:44.50 | 3rd Qu.:169000 | 3rd Qu.:100.00 | 3rd Qu.:313.0 | |
| Max. :352.0 | Max. :174.00 | Max. :9.400 | Max. :337.0 | Max. :591.0 | Max. :752.00 | Max. :766.0 | Max. :1884.0 | Max. :492.0 | Max. :8.300 | Max. :9.100 | Max. :7.700 | Max. :150.0 | Max. :6.000 | Max. :12640 | Max. :57.00 | Max. :297000 | Max. :118.00 | Max. :610.0 | |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :1 |
# Calcular la frecuencia de cada combinación de género y fibrosis
frecuencia_genero_fibrosis <- table(datos$fibrosis, datos$genero)
# Convertir los resultados en un data frame para facilitar el manejo
frecuencia_df <- as.data.frame(frecuencia_genero_fibrosis)
colnames(frecuencia_df) <- c("Fibrosis", "Genero", "Cantidad")
# Graficar con etiquetas de valores
ggplot(frecuencia_df, aes(x = Fibrosis, y = Cantidad, fill = Genero)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(label = Cantidad),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 3,
color = "black") +
labs(x = "Fibrosis", y = "Cantidad", fill = "Género",
title = "Distribución de género en cada categoría de fibrosis") +
theme_minimal()
generar_grafico_barras <- function(datos, variable, agrupacion1, agrupacion2, etiquetas_x = NULL, etiquetas_y = NULL, etiquetas_fill = NULL, titulo = NULL) {
promedio_variable <- datos %>%
group_by({{ agrupacion1 }}, {{ agrupacion2 }}) %>%
summarize(promedio_variable = mean({{ variable }}, na.rm = TRUE))
plot <- ggplot(promedio_variable, aes(x = promedio_variable, y = {{ agrupacion1 }}, fill = {{ agrupacion2 }})) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(label = round(promedio_variable, 2)), position = position_dodge(width = 0.9), vjust = 0.5, hjust = 1) +
labs(x = ifelse(is.null(etiquetas_x), "Promedio de Variable", etiquetas_x),
y = ifelse(is.null(etiquetas_y), "Fibrosis", etiquetas_y),
fill = ifelse(is.null(etiquetas_fill), "Género", etiquetas_fill),
title = ifelse(is.null(titulo), paste("Promedio de", deparse(substitute(variable)), "por Categoría de Fibrosis y Genéro"), titulo)) +
theme_minimal()
return(plot)
}#Barras promedio para Glucemia
grafico <- generar_grafico_barras(datos, Glucemia, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para Urea
grafico <- generar_grafico_barras(datos, Urea, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para Creatinina
grafico <- generar_grafico_barras(datos, Creatinina, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para Colesterol
grafico <- generar_grafico_barras(datos, Colesterol, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para Triglicéridos
grafico <- generar_grafico_barras(datos, Triglicéridos, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para GOT
grafico <- generar_grafico_barras(datos, GOT, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para GPT
grafico <- generar_grafico_barras(datos,GPT, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para GGT
grafico <- generar_grafico_barras(datos, GGT, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para Fosfatasa Alcalina
grafico <- generar_grafico_barras(datos, Fosfatasa.Alcalina, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para bilirrubina.total
grafico <- generar_grafico_barras(datos, bilirrubina.total, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para proteínas.totales
grafico <- generar_grafico_barras(datos, proteínas.totales, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para albúmina
grafico <- generar_grafico_barras(datos, albúmina, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para sodio
grafico <- generar_grafico_barras(datos, sodio, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para potasio
grafico <- generar_grafico_barras(datos, potasio, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para leucocitos
grafico <- generar_grafico_barras(datos, leucocitos, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para hematocrito
grafico <- generar_grafico_barras(datos, hematocrito, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para plaquetas
grafico <- generar_grafico_barras(datos, plaquetas, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para Índice.de.Quick
grafico <- generar_grafico_barras(datos, Índice.de.Quick, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#Barras promedio para fibrinógeno
grafico <- generar_grafico_barras(datos, fibrinógeno, fibrosis, genero)## `summarise()` has grouped output by 'fibrosis'. You can override using the
## `.groups` argument.print(grafico)
#promedio de Glucemia
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(Glucemia, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Glucemia",
title = "Promedio de Glucemia por Categoría de Fibrosis") +
theme_minimal()
#promedio de Urea
promedio_Urea <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_Urea = mean(Urea, na.rm = TRUE))
#Gráfico
ggplot(promedio_Urea, aes(x = fibrosis, y = promedio_Urea, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_Urea, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Urea",
title = "Promedio de Urea por Categoría de Fibrosis") +
theme_minimal()
#promedio de Creatinina
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(Creatinina, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Creatinina",
title = "Promedio de Creatinina por Categoría de Fibrosis") +
theme_minimal()
#promedio de Colesterol
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(Colesterol, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Colesterol",
title = "Promedio de Colesterol por Categoría de Fibrosis") +
theme_minimal()
#promedio de Triglicéridos
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(Triglicéridos, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Triglicéridos",
title = "Promedio de Triglicéridos por Categoría de Fibrosis") +
theme_minimal()
#promedio GOT
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(GOT, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de GOT",
title = "Promedio de GOT por Categoría de Fibrosis") +
theme_minimal()
#promedio GPT
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(GPT, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de GPT",
title = "Promedio de GPT por Categoría de Fibrosis") +
theme_minimal()
#promedio GGT
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(GGT, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de GGT",
title = "Promedio de GGT por Categoría de Fibrosis") +
theme_minimal()
#promedio Fosfatasa Alcalina
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(Fosfatasa.Alcalina, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Fosfatasa Alcalina",
title = "Promedio de Fosfatasa Alcalina por Categoría de Fibrosis") +
theme_minimal()
#promedio Bilirrubina total
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(bilirrubina.total, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Bilirrubina Total",
title = "Promedio de Bilirrubina Total por Categoría de Fibrosis") +
theme_minimal()
#promedio proteínas totales
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(proteínas.totales, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Proteínas totales ",
title = "Promedio de Proteínas totales por Categoría de Fibrosis") +
theme_minimal()
#promedio albúmina
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(albúmina, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Albúmia",
title = "Promedio de Albúmia por Categoría de Fibrosis") +
theme_minimal()
#promedio sodio
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(sodio, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Sodio",
title = "Promedio de Sodio por Categoría de Fibrosis") +
theme_minimal()
#promedio potasio
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(potasio, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Potasio",
title = "Promedio de Potasio por Categoría de Fibrosis") +
theme_minimal()
#promedio leucocitos
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(leucocitos, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Leucocitos",
title = "Promedio de Leucocitos por Categoría de Fibrosis") +
theme_minimal()
#promedio hematocrito
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(hematocrito, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Hematocrito",
title = "Promedio de Hematocrito por Categoría de Fibrosis") +
theme_minimal()
#promedio plaquetas
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(plaquetas, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Plaquetas",
title = "Promedio de Plaquetas por Categoría de Fibrosis") +
theme_minimal()
#promedio Índice.de.Quick
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(Índice.de.Quick, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Índice.de.Quick",
title = "Promedio de Índice de Quick por Categoría de Fibrosis") +
theme_minimal()
#promedio fibrinógeno
promedio_variable <- datos %>%
group_by(fibrosis) %>%
summarize(promedio_variable = mean(fibrinógeno, na.rm = TRUE))
#Gráfico
ggplot(promedio_variable, aes(x = fibrosis, y = promedio_variable, fill = fibrosis)) +
geom_col() +
geom_text(aes(label = round(promedio_variable, 2)), vjust = -0.5, hjust = 0.5, color = "black", size = 3) +
labs(x = "Fibrosis", y = "Promedio de Fibrinógeno",
title = "Promedio de Fibrinógeno por Categoría de Fibrosis") +
theme_minimal()
compute_summary_stats <- function(data, group_var, target_var) {
summary_stats <- data %>%
group_by({{ group_var }}) %>%
summarize(
Q1 = quantile({{ target_var }}, 0.25),
median = median({{ target_var }}),
Q3 = quantile({{ target_var }}, 0.75),
min = min({{ target_var }}),
max = max({{ target_var }}),
outliers = list(boxplot.stats({{ target_var }})$out)
)
# Convertir los valores atípicos en texto
summary_stats$outliers <- sapply(summary_stats$outliers, function(x) paste(x, collapse = ", "))
return(summary_stats)
}#boxplot
ggplot(datos, aes(x = fibrosis, y = Glucemia)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis Hepática", y = "Glucemia") +
ggtitle("Boxplot de Glucemia según Fibrosis")
variable <- "Glucemia"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Glucemia")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 90.00 | 102.0 | 122.00 | 69 | 290 | 290, 228, 172, 205, 226, 181, 218, 271, 192, 235, 237, 177, 203, 204 |
| importante | 84.50 | 120.5 | 133.00 | 14 | 352 | 317, 352 |
| leve | 86.00 | 101.0 | 121.00 | 45 | 280 | 280, 260, 176, 192, 211, 225, 190 |
| moderada | 87.75 | 96.5 | 117.25 | 68 | 162 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = Urea)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis Hepática", y = "Urea") +
ggtitle("Boxplot de Urea según Fibrosis")
variable <- "Urea"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Urea")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 25.00 | 33.0 | 45.00 | 12 | 84 | 84 |
| importante | 21.75 | 26.5 | 34.00 | 18 | 58 | 58 |
| leve | 22.25 | 29.0 | 43.00 | 13 | 174 | 87, 174 |
| moderada | 22.00 | 29.0 | 45.25 | 11 | 106 | 106, 89 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = Creatinina)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Creatinina") +
ggtitle("Boxplot de Creatinina según Fibrosis")
variable <- "Creatinina"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Creatinina")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 1.000 | 1.200 | 1.500 | 0.40 | 4.3 | 3, 2.5, 2.8, 4.3, 2.3 |
| importante | 0.875 | 1.040 | 1.225 | 0.60 | 1.7 | |
| leve | 0.800 | 1.055 | 1.200 | 0.50 | 9.4 | 1.9, 9.4, 1.8, 2 |
| moderada | 0.860 | 1.000 | 1.200 | 0.49 | 2.2 | 2.2, 2, 1.8 |
``
#boxplot
ggplot(datos, aes(x = fibrosis, y = Colesterol))+
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Colesterol") +
ggtitle("Boxplot de Colesterol según Fibrosis")
variable <- "Colesterol"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Colesterol")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 162.00 | 184.0 | 202.00 | 20 | 337 | 20, 337, 264, 85, 296, 88 |
| importante | 135.25 | 172.0 | 179.50 | 92 | 245 | |
| leve | 161.00 | 182.5 | 208.75 | 82 | 316 | 291, 316, 82 |
| moderada | 148.25 | 174.0 | 203.00 | 88 | 270 |
``
#boxplot
ggplot(datos, aes(x = fibrosis, y = Triglicéridos))+
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Triglicéridos") +
ggtitle("Boxplot de Triglicéridos según Fibrosis")
variable <- "Triglicéridos"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Triglicéridos")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 106.00 | 134 | 199.00 | 49.0 | 591 | 343, 591, 427, 447, 344 |
| importante | 131.75 | 160 | 194.00 | 50.0 | 448 | 428, 448 |
| leve | 102.25 | 125 | 181.00 | 7.5 | 465 | 465, 351, 419, 365, 375, 326 |
| moderada | 99.25 | 152 | 204.75 | 56.0 | 344 |
``
#boxplot
ggplot(datos, aes(x = fibrosis, y = GOT)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "GOT") +
ggtitle("Boxplot de GOT según Fibrosis")
variable <- "GOT"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen GOT")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 27.0 | 40.0 | 64.00 | 13 | 309 | 173, 136, 217, 120, 195, 139, 309, 144, 208, 130, 172, 136 |
| importante | 79.5 | 116.5 | 156.00 | 40 | 524 | 313, 524, 360 |
| leve | 44.5 | 62.5 | 116.75 | 13 | 331 | 331, 239, 241, 228 |
| moderada | 53.0 | 102.5 | 184.25 | 21 | 752 | 752, 407, 518, 583, 572 |
``
#boxplot
ggplot(datos, aes(x = fibrosis, y = GPT )) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "GPT") +
ggtitle("Boxplot de GPT según Fibrosis")
variable <- "GPT"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen GPT")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 36.00 | 60.0 | 97.00 | 19 | 350 | 202, 350, 226, 283, 315, 283, 309, 347, 278, 245 |
| importante | 106.25 | 143.0 | 236.75 | 31 | 589 | 589, 479 |
| leve | 60.75 | 95.5 | 149.50 | 16 | 568 | 568, 295, 446, 446, 328, 311, 337, 296, 358, 374 |
| moderada | 66.75 | 116.5 | 255.25 | 20 | 766 | 596, 766, 570 |
``
#boxplot
ggplot(datos, aes(x = fibrosis, y = GGT)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "GGT") +
ggtitle("Boxplot de GGT según Fibrosis")
variable <- "GGT"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen GGT")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 31.00 | 57.0 | 91.00 | 13 | 696 | 315, 411, 296, 188, 368, 351, 182, 355, 369, 696, 379, 272 |
| importante | 51.25 | 73.5 | 122.75 | 19 | 176 | |
| leve | 45.25 | 85.5 | 179.75 | 14 | 1884 | 807, 524, 719, 477, 1884, 531, 428, 642, 383, 423, 467 |
| moderada | 34.00 | 93.0 | 179.00 | 12 | 628 | 415, 628 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = Fosfatasa.Alcalina)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Fosfatasa Alcalina") +
ggtitle("Boxplot de Fosfatasa Alcalina según Fibrosis")
variable <- "Fosfatasa.Alcalina"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Fosfatasa Alcalina")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 67.0 | 83.0 | 110.00 | 32 | 492 | 190, 182, 258, 236, 492, 182, 350 |
| importante | 97.5 | 109.0 | 151.75 | 63 | 213 | |
| leve | 72.5 | 97.5 | 129.50 | 27 | 414 | 318, 275, 284, 414, 248, 243 |
| moderada | 76.5 | 101.5 | 130.50 | 48 | 260 | 260, 236, 231 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = bilirrubina.total)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Bilirrubina Total") +
ggtitle("Boxplot de Bilirrubina Total según Fibrosis")
variable <- "bilirrubina.total"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Bilirrubina Total")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 0.700 | 0.92 | 1.2000 | 0.30 | 3.2 | 2, 2, 3.2, 3.1 |
| importante | 0.700 | 1.00 | 1.5975 | 0.50 | 4.0 | 3.8, 4, 3.9 |
| leve | 0.700 | 1.00 | 1.4000 | 0.23 | 6.9 | 2.9, 2.5, 6.9, 2.6, 2.6, 3.4, 2.8, 5.9 |
| moderada | 0.675 | 0.93 | 1.3250 | 0.40 | 8.3 | 2.5, 3.24, 8.3 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = proteínas.totales)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Proteínas totales") +
ggtitle("Boxplot de Proteínas totales según Fibrosis")
variable <- "proteínas.totales"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Proteínas Totales")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 6.700 | 7.10 | 7.5 | 4.6 | 8.6 | 4.6, 5.3, 5.4, 5.3 |
| importante | 6.950 | 7.25 | 7.7 | 5.7 | 8.2 | 5.7 |
| leve | 6.725 | 7.10 | 7.4 | 6.2 | 9.1 | 9.1, 9 |
| moderada | 6.890 | 7.20 | 7.6 | 5.6 | 8.5 | 5.6 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = albúmina)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Albúmina") +
ggtitle("Boxplot de Albúmina según Fibrosis")
variable <- "albúmina"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Albúmina")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 4.2 | 4.40 | 4.600 | 2.6 | 7.7 | 5.3, 3.2, 7.7, 2.7, 2.6 |
| importante | 3.5 | 3.85 | 4.225 | 2.9 | 4.6 | |
| leve | 4.0 | 4.30 | 4.500 | 3.2 | 5.0 | 3.2 |
| moderada | 3.7 | 4.05 | 4.300 | 2.9 | 4.7 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = sodio)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Sodio") +
ggtitle("Boxplot de Sodio según Fibrosis")
variable <- "sodio"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Sodio")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 138 | 140 | 142 | 128 | 150 | 150, 131, 128 |
| importante | 138 | 140 | 141 | 128 | 147 | 128, 147 |
| leve | 138 | 140 | 141 | 133 | 145 | 133 |
| moderada | 137 | 139 | 141 | 132 | 145 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = potasio)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Potasio") +
ggtitle("Boxplot de Potasio según Fibrosis")
variable <- "potasio"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Potasio")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 4.3 | 4.60 | 4.800 | 3.6 | 6.0 | 5.9, 6, 5.8, 5.6 |
| importante | 4.1 | 4.45 | 4.725 | 3.4 | 5.4 | |
| leve | 4.3 | 4.50 | 4.700 | 3.5 | 6.0 | 6, 5.5, 3.6, 5.5, 5.6, 3.5, 5.5, 5.6 |
| moderada | 4.2 | 4.40 | 4.800 | 3.8 | 6.0 | 6, 5.8 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = leucocitos)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Leucocitos") +
ggtitle("Boxplot de Leucocitos según Fibrosis")
variable <- "leucocitos"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Leucocitos")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 4430.0 | 5650 | 7000.0 | 2440 | 12640 | 11610, 11010, 12640 |
| importante | 3307.5 | 4795 | 6837.5 | 1980 | 11240 | |
| leve | 4217.5 | 5540 | 6657.5 | 2240 | 11950 | 11950, 11090, 10420, 10490 |
| moderada | 3695.0 | 4520 | 6085.0 | 1700 | 8690 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = hematocrito)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Hematocrito") +
ggtitle("Boxplot de Hematocrito según Fibrosis")
variable <- "hematocrito"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Hematocrito")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 38.00 | 41 | 45.00 | 24.0 | 55 | 24 |
| importante | 37.75 | 41 | 45.00 | 30.0 | 47 | |
| leve | 39.00 | 42 | 44.55 | 31.8 | 57 | 56.8, 57 |
| moderada | 37.90 | 40 | 43.00 | 31.0 | 53 | 53 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = plaquetas)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Plaquetas") +
ggtitle("Boxplot de Plaquetas según Fibrosis")
variable <- "plaquetas"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Plaquetas")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 112000 | 140000 | 168000 | 10000 | 261000 | 261000, 10000, 258000 |
| importante | 81500 | 103000 | 124500 | 14000 | 229000 | 229000 |
| leve | 116000 | 149500 | 175000 | 35000 | 259000 | |
| moderada | 91750 | 135500 | 162750 | 42000 | 297000 | 297000 |
#boxplot
ggplot(datos, aes(x = fibrosis, y = Índice.de.Quick)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Índice de Quick") +
ggtitle("Boxplot de Índice de Quick según Fibrosis")
variable <- "Índice.de.Quick"
summary_stats <- compute_summary_stats(datos, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen índice de Quick")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 100 | 100.0 | 100.0 | 53 | 118 | 84, 103, 99, 99, 96, 98, 99, 102, 95, 96, 90, 92, 78, 96, 80, 99, 96, 70, 90, 98, 98, 84, 88, 95, 98, 53, 80, 86, 99, 118, 90, 94, 77.7, 94 |
| importante | 76 | 86.5 | 98.5 | 45 | 100 | |
| leve | 98 | 100.0 | 100.0 | 71 | 100 | 76, 92, 92, 87, 93, 94, 90, 83, 94, 92, 85, 94, 76, 87, 89, 86, 90, 71, 86 |
| moderada | 90 | 98.5 | 100.0 | 53 | 100 | 53, 63, 56, 60 |
#boxplot
datos_sin_na_nan <- datos[!is.na(datos$fibrinógeno) & !is.nan(datos$fibrinógeno), ]
ggplot(datos_sin_na_nan, aes(x = fibrosis, y = fibrinógeno)) +
geom_boxplot(fill = "skyblue", color = "blue") +
labs(x = "Fibrosis", y = "Fibrinógeno") +
ggtitle("Boxplot de Fibrinógeno según Fibrosis")
variable <- "fibrinógeno"
summary_stats <- compute_summary_stats(datos_sin_na_nan, fibrosis, !!as.name(variable))
kable(summary_stats, caption = "Resumen Fibrinógeno")| fibrosis | Q1 | median | Q3 | min | max | outliers |
|---|---|---|---|---|---|---|
| ausencia | 240 | 276.0 | 312.00 | 163 | 610 | 438, 458, 531, 538, 455, 610 |
| importante | 196 | 230.0 | 258.75 | 143 | 387 | 387 |
| leve | 227 | 274.0 | 324.00 | 125 | 471 | 471 |
| moderada | 217 | 265.5 | 298.25 | 123 | 399 |
tabla_frecuencia_agrupada <- function(datos) {
# Calcular frecuencia absoluta para la variable "datos"
frecuencia_absoluta <- table(datos)
# Calcular la frecuencia relativa
frecuencia_relativa <- prop.table(frecuencia_absoluta)
# Calcular la frecuencia porcentual
frecuencia_porcentual <- frecuencia_relativa * 100
# Calcular la frecuencia absoluta acumulada
frecuencia_acumulada <- cumsum(frecuencia_absoluta)
# Calcular la frecuencia relativa acumulada
frecuencia_relativa_acumulada <- cumsum(frecuencia_relativa)
# Calcular la frecuencia porcentual acumulada
frecuencia_porcentual_acumulada <- cumsum(frecuencia_porcentual)
# Crear el dataframe con los resultados
tabla_resultado <- data.frame(
Frecuencia_Absoluta = c(frecuencia_absoluta),
Frecuencia_Relativa = c(frecuencia_relativa),
Frecuencia_Porcentual = c(frecuencia_porcentual),
Frecuencia_Absoluta_Acumulada = c(frecuencia_acumulada),
Frecuencia_Relativa_Acumulada = c(frecuencia_relativa_acumulada),
Frecuencia_Porcentual_Acumulada = c(frecuencia_porcentual_acumulada)
)
tabla_resultado <- tabla_resultado[, !grepl(".frecuencias", colnames(tabla_resultado))]
colnames(tabla_resultado) <- c("Frecuencia Absoluta",
"Frecuencia Relativa",
"Frecuencia Porcentual",
"Frecuencia Absoluta Acumulada",
"Frecuencia Relativa Acumulada",
"Frecuencia Porcentual Acumulada")
return(tabla_resultado)
}
# Ejemplo de uso de la función con la variable "género"
resultado <- tabla_frecuencia_agrupada(datos$genero)
# Imprimir el resultado
kable(resultado)| Frecuencia Absoluta | Frecuencia Relativa | Frecuencia Porcentual | Frecuencia Absoluta Acumulada | Frecuencia Relativa Acumulada | Frecuencia Porcentual Acumulada | |
|---|---|---|---|---|---|---|
| Hombre | 173 | 0.5356037 | 53.56037 | 173 | 0.5356037 | 53.56037 |
| Mujer | 150 | 0.4643963 | 46.43963 | 323 | 1.0000000 | 100.00000 |
tabla_frecuencia_agrupada <- function(datos) {
# Calcular frecuencia absoluta para la variable "datos"
frecuencia_absoluta <- table(datos)
# Calcular la frecuencia relativa
frecuencia_relativa <- prop.table(frecuencia_absoluta)
# Calcular la frecuencia porcentual
frecuencia_porcentual <- frecuencia_relativa * 100
# Calcular la frecuencia absoluta acumulada
frecuencia_acumulada <- cumsum(frecuencia_absoluta)
# Calcular la frecuencia relativa acumulada
frecuencia_relativa_acumulada <- cumsum(frecuencia_relativa)
# Calcular la frecuencia porcentual acumulada
frecuencia_porcentual_acumulada <- cumsum(frecuencia_porcentual)
# Crear el dataframe con los resultados
tabla_resultado <- data.frame(
Frecuencia_Absoluta = c(frecuencia_absoluta),
Frecuencia_Relativa = c(frecuencia_relativa),
Frecuencia_Porcentual = c(frecuencia_porcentual),
Frecuencia_Absoluta_Acumulada = c(frecuencia_acumulada),
Frecuencia_Relativa_Acumulada = c(frecuencia_relativa_acumulada),
Frecuencia_Porcentual_Acumulada = c(frecuencia_porcentual_acumulada)
)
tabla_resultado <- tabla_resultado[, !grepl(".frecuencias", colnames(tabla_resultado))]
colnames(tabla_resultado) <- c("Frecuencia Absoluta",
"Frecuencia Relativa",
"Frecuencia Porcentual",
"Frecuencia Absoluta Acumulada",
"Frecuencia Relativa Acumulada",
"Frecuencia Porcentual Acumulada")
return(tabla_resultado)
}
# Ejemplo de uso de la función con la variable "género"
resultado <- tabla_frecuencia_agrupada(datos$fibrosis)
# Imprimir el resultado
kable(resultado)| Frecuencia Absoluta | Frecuencia Relativa | Frecuencia Porcentual | Frecuencia Absoluta Acumulada | Frecuencia Relativa Acumulada | Frecuencia Porcentual Acumulada | |
|---|---|---|---|---|---|---|
| ausencia | 141 | 0.4365325 | 43.65325 | 141 | 0.4365325 | 43.65325 |
| importante | 20 | 0.0619195 | 6.19195 | 161 | 0.4984520 | 49.84520 |
| leve | 110 | 0.3405573 | 34.05573 | 271 | 0.8390093 | 83.90093 |
| moderada | 52 | 0.1609907 | 16.09907 | 323 | 1.0000000 | 100.00000 |
#dispersión para Glucemia
Glucemia.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Glucemia, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Glucemia", color = "Categoría de Fibrosis", title = "Comparación de Glucemia por categoría de fibrosis") +
theme_minimal()
#dispersión para Urea
Urea.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Urea, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Urea", color = "Categoría de Fibrosis", title = "Comparación de Urea por categoría de fibrosis") +
theme_minimal()
#dispersión para Creatinina
Creatinina.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Creatinina, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Creatinina", color = "Categoría de Fibrosis", title = "Comparación de Creatinina por categoría de fibrosis") +
theme_minimal()
#dispersión para Colesterol
Colesterol.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Colesterol, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Colesterol", color = "Categoría de Fibrosis", title = "Comparación de Colesterol por categoría de fibrosis") +
theme_minimal()
#dispersión para Triglicéridos
Triglicéridos.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Triglicéridos, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Triglicéridos", color = "Categoría de Fibrosis", title = "Comparación de Triglicéridos por categoría de fibrosis") +
theme_minimal()
#dispersión para GOT
GOT.GRAPH <- ggplot(datos, aes(x = fibrosis, y = GOT, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "GOT", color = "Categoría de Fibrosis", title = "Comparación de GOT por categoría de fibrosis") +
theme_minimal()
#dispersión para GPT
GPT.GRAPH <-ggplot(datos, aes(x = fibrosis, y = GPT, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "GPT", color = "Categoría de Fibrosis", title = "Comparación de GPT por categoría de fibrosis") +
theme_minimal()
#dispersión para GGT
GGT.GRAPH <- ggplot(datos, aes(x = fibrosis, y = GGT, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "GGT", color = "Categoría de Fibrosis", title = "Comparación de GGT por categoría de fibrosis") +
theme_minimal()
#dispersión para Fosfatasa Alcalina
Fosfatasa.Alcalina.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Fosfatasa.Alcalina, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Fosfatasa Alcalina", color = "Categoría de Fibrosis", title = "Comparación de Fosfatasa Alcalina por categoría de fibrosis") +
theme_minimal()
#dispersión para bilirrubina total
bilirrubina.total.GRAPH <- ggplot(datos, aes(x = fibrosis, y = bilirrubina.total, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Bilirrubina total", color = "Categoría de Fibrosis", title = "Comparación de Bilirrubina total por categoría de fibrosis") +
theme_minimal()
#dispersión para proteínas totales
proteínas.totales.GRAPH <- ggplot(datos, aes(x = fibrosis, y = proteínas.totales, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Proteínas totales", color = "Categoría de Fibrosis", title = "Comparación de Proteínas totales por categoría de fibrosis") +
theme_minimal()
#dispersión para albúmina
albúmina.GRAPH <- ggplot(datos, aes(x = fibrosis, y = albúmina, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Albúmina", color = "Categoría de Fibrosis", title = "Comparación de Albúmina por categoría de fibrosis") +
theme_minimal()
#dispersión para sodio
sodio.GRAPH <- ggplot(datos, aes(x = fibrosis, y = sodio, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Sodio", color = "Categoría de Fibrosis", title = "Comparación de Sodio por categoría de fibrosis") +
theme_minimal()
#dispersión para Potasio
potasio.GRAPH <- ggplot(datos, aes(x = fibrosis, y = potasio, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Potasio", color = "Categoría de Fibrosis", title = "Comparación de Potasio por categoría de fibrosis") +
theme_minimal()
#dispersión para leucocitos
leucocitos.GRAPH <- ggplot(datos, aes(x = fibrosis, y = leucocitos, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Leucocitos", color = "Categoría de Fibrosis", title = "Comparación de Leucocitos por categoría de fibrosis") +
theme_minimal()
#dispersión para Hematocrito
hematocrito.GRAPH <- ggplot(datos, aes(x = fibrosis, y = hematocrito, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Hematocrito ", color = "Categoría de Fibrosis", title = "Comparación de Hematocrito por categoría de fibrosis") +
theme_minimal()
#dispersión para plaquetas
plaquetas.GRAPH <- ggplot(datos, aes(x = fibrosis, y = plaquetas, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Plaquetas", color = "Categoría de Fibrosis", title = "Comparación de Plaquetas por categoría de fibrosis") +
theme_minimal()
#dispersión para Índice de Quick
Índice.de.Quick.GRAPH <- ggplot(datos, aes(x = fibrosis, y = Índice.de.Quick, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Índice de Quick", color = "Categoría de Fibrosis", title = "Comparación de Índice de Quick por categoría de fibrosis") +
theme_minimal()
#dispersión para fibrinógeno
fibrinógeno.GRAPH <- ggplot(datos, aes(x = fibrosis, y = fibrinógeno, color = fibrosis)) +
geom_point(alpha = 0.7, size = 3) +
labs(x = "Categoría de Fibrosis", y = "Fibrinógeno", color = "Categoría de Fibrosis", title = "Comparación de Fibrinógeno por categoría de fibrosis") +
theme_minimal()
print(Glucemia.GRAPH)
print(Urea.GRAPH)
print(Creatinina.GRAPH)
print(Colesterol.GRAPH)
print(Triglicéridos.GRAPH)
print(GOT.GRAPH)
print(GPT.GRAPH)
print(GGT.GRAPH)
print(Fosfatasa.Alcalina.GRAPH)
print(bilirrubina.total.GRAPH)
print(proteínas.totales.GRAPH)
print(albúmina.GRAPH)
print(sodio.GRAPH)
print(potasio.GRAPH)
print(leucocitos.GRAPH)
print(plaquetas.GRAPH)
print(Índice.de.Quick.GRAPH)
print(fibrinógeno.GRAPH)## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Glucemia, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Urea, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Creatinina, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Colesterol, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Triglicéridos, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= GOT, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= GPT, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= GGT, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Fosfatasa.Alcalina, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= bilirrubina.total, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= proteínas.totales, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= albúmina, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= sodio, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= potasio, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= leucocitos, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= hematocrito, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= plaquetas, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= Índice.de.Quick, color = genero)) +
facet_wrap(~ fibrosis)
ggplot() +
geom_point(datos, mapping=aes(x=Individuo, y= fibrinógeno, color = genero)) +
facet_wrap(~ fibrosis)## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(datos, aes(x=Individuo, y= proteínas.totales, color= bilirrubina.total)) +
geom_point() +
facet_wrap(~fibrosis) +
scale_color_gradient(low = "blue", high = "red") 
ggplot(datos, aes( x= Colesterol, y = Triglicéridos, color = Colesterol)) +
geom_point() +
facet_wrap(~fibrosis) +
scale_color_gradient(low = "blue", high = "red") 
ggplot(datos, aes( x= Índice.de.Quick, y = fibrinógeno, color = Índice.de.Quick)) +
geom_point() +
facet_wrap(~fibrosis) +
scale_color_gradient(low = "blue", high = "red") ## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
tablafrec <- function(datos, variable, nombre_variable = NULL) {
TF <- as.data.frame(table(cut(datos[[variable]], breaks = 10)))
tablafrec <- transform(TF,
FreqAc = cumsum(Freq),
Rel = round(prop.table(Freq), 2),
RelAc = round(cumsum(prop.table(Freq)), 2),
FreqPer = round(prop.table(Freq), 2) * 100,
FreqPerAc = round(cumsum(prop.table(Freq)), 2) * 100
)
if (!is.null(nombre_variable)) {
names(tablafrec)[1] <- nombre_variable
}
return(tablafrec)
}Glusemia <- datos$Glucemia
h <- hist(Glusemia, xlim=c(0,400), ylim=c(0,200), col="darkseagreen1", main="Histograma Glusemia")
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
Urea <- datos$Urea
h <- hist(Urea, xlim=c(0,190), ylim=c(0,200), col="darkseagreen2", main="Histograma Urea")
eje_x <- seq(0, 200, by = 20)
axis(1, at = eje_x)
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
Creatinina <- datos$Creatinina
h <- hist(Creatinina, xlim=c(0,10), ylim=c(0,200), col="darkseagreen2", main="Histograma Creatinina")
eje_x <- seq(0, 10)
axis(1, at = eje_x)
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
Colesterol <- datos$Colesterol
h <- hist(Colesterol, xlim=c(0,350), ylim=c(0,200), col="darkseagreen2", main="Histograma Colesterol")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
Triglicéridos <- datos$Triglicéridos
h <- hist(Triglicéridos, xlim=c(0,600), ylim=c(0,120), col="darkseagreen2", main="Histograma Triglicéridos")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
GOT <- datos$GOT
h <- hist(GOT, xlim=c(0,800), ylim=c(0,250), col="darkseagreen2", main="Histograma GOT")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
GPT <- datos$GPT
h <- hist(GPT, xlim=c(0,800), ylim=c(0,200), col="darkseagreen2", main="Histograma GPT")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
GGT <- datos$GGT
h <- hist(GGT, xlim=c(0,2000), ylim=c(0,300), col="darkseagreen2", main="Histograma GGT")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
Fosfatasa.Alcalina <- datos$Fosfatasa.Alcalina
h <- hist(Fosfatasa.Alcalina, xlim=c(0,500), ylim=c(0,200), col="darkseagreen2", main="Histograma Fosfatasa Alcalina")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
bilirrubina.total <- datos$bilirrubina.total
intervalos <- seq(0, 10, by = 0.5)
h <- hist(bilirrubina.total, breaks = intervalos, xlim=c(0.2,10), ylim=c(0,200), col="darkseagreen2", main="Histograma Bilirrubina total")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
proteínas.totales <- datos$proteínas.totales
h <- hist(proteínas.totales, xlim=c(4.5,9.5), ylim=c(0,120), col="darkseagreen2", main="Histograma Proteínas totales")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
albúmina <- datos$albúmina
h <- hist(albúmina, xlim=c(2,8), ylim=c(0,200), col="darkseagreen2", main="Histograma Albúmina")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
sodio <- datos$sodio
h <- hist(sodio, xlim=c(125,150), ylim=c(0,120), col="darkseagreen2", main="Histograma Sodio")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
potasio <- datos$potasio
h <- hist(potasio, xlim=c(3,6), ylim=c(0,80), col="darkseagreen2", main="Histograma Potasio")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
leucocitos <- datos$leucocitos
h <- hist(leucocitos, xlim=c(1500, 12750), ylim=c(0,80), col="darkseagreen2", main="Histograma Leucocitos")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
hematocrito <- datos$hematocrito
h <- hist(hematocrito, xlim=c(20,60), ylim=c(0,130), col="darkseagreen2", main="Histograma Hematocrito")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
plaquetas <- datos$plaquetas
h <- hist(plaquetas, xlim=c(10000,300000), ylim=c(0,150), col="darkseagreen2", main="Histograma Plaquetas")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
Índice.de.Quick <- datos$Índice.de.Quick
h <- hist(Índice.de.Quick, xlim=c(40,120), ylim=c(0,300), col="darkseagreen2", main="Histograma Índice de Quick")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
fibrinógeno <- datos$fibrinógeno
h <- hist(fibrinógeno, xlim=c(120,620), ylim=c(0,120), col="darkseagreen2", main="Histograma Fibrinógeno")
#etiquetas
text(h$mids, h$counts, labels=h$counts, adj=c(0.5, -0.5))
TFUrea <- as.data.frame(table(Urea = cut(datos$Urea, breaks = 10)))
TFUrea <- transform(TFUrea,
FreqAc = cumsum(Freq),
Rel = round(prop.table(Freq), 2),
RelAc = round(cumsum(prop.table(Freq)), 2)
)
kable(TFUrea)| Urea | Freq | FreqAc | Rel | RelAc |
|---|---|---|---|---|
| (10.8,27.3] | 132 | 132 | 0.41 | 0.41 |
| (27.3,43.6] | 111 | 243 | 0.34 | 0.75 |
| (43.6,59.9] | 54 | 297 | 0.17 | 0.92 |
| (59.9,76.2] | 21 | 318 | 0.07 | 0.98 |
| (76.2,92.5] | 3 | 321 | 0.01 | 0.99 |
| (92.5,109] | 1 | 322 | 0.00 | 1.00 |
| (109,125] | 0 | 322 | 0.00 | 1.00 |
| (125,141] | 0 | 322 | 0.00 | 1.00 |
| (141,158] | 0 | 322 | 0.00 | 1.00 |
| (158,174] | 1 | 323 | 0.00 | 1.00 |
#Tabla de Frecuencia
Tabla.frec <- function(datos, variable, num_breaks = 10) {
Variable <- as.data.frame(table(Var = cut(datos[[variable]], breaks = num_breaks)))
Variable <- transform(Variable,
Rel = round(prop.table(Freq), 3),
Rel.per = round(prop.table(Freq) * 100, 3),
FreqAc = cumsum(Freq),
RelAc = round(cumsum(prop.table(Freq)), 3),
Freq.PerAc = round(cumsum(prop.table(Freq) * 100), 3)
)
return(Variable)
}resultados <- Tabla.frec(datos, "Glucemia")
kable(resultados, caption = "Tabla de frecuencia Glucemia")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (13.7,47.8] | 2 | 0.006 | 0.619 | 2 | 0.006 | 0.619 |
| (47.8,81.6] | 37 | 0.115 | 11.455 | 39 | 0.121 | 12.074 |
| (81.6,115] | 188 | 0.582 | 58.204 | 227 | 0.703 | 70.279 |
| (115,149] | 58 | 0.180 | 17.957 | 285 | 0.882 | 88.235 |
| (149,183] | 18 | 0.056 | 5.573 | 303 | 0.938 | 93.808 |
| (183,217] | 8 | 0.025 | 2.477 | 311 | 0.963 | 96.285 |
| (217,251] | 6 | 0.019 | 1.858 | 317 | 0.981 | 98.142 |
| (251,284] | 3 | 0.009 | 0.929 | 320 | 0.991 | 99.071 |
| (284,318] | 2 | 0.006 | 0.619 | 322 | 0.997 | 99.690 |
| (318,352] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "Urea")
kable(resultados, caption = "Tabla de frecuencia Urea")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (10.8,27.3] | 132 | 0.409 | 40.867 | 132 | 0.409 | 40.867 |
| (27.3,43.6] | 111 | 0.344 | 34.365 | 243 | 0.752 | 75.232 |
| (43.6,59.9] | 54 | 0.167 | 16.718 | 297 | 0.920 | 91.950 |
| (59.9,76.2] | 21 | 0.065 | 6.502 | 318 | 0.985 | 98.452 |
| (76.2,92.5] | 3 | 0.009 | 0.929 | 321 | 0.994 | 99.381 |
| (92.5,109] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (109,125] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (125,141] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (141,158] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (158,174] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "Creatinina")
kable(resultados, caption = "Tabla de frecuencia Creatinina")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (0.391,1.3] | 238 | 0.737 | 73.684 | 238 | 0.737 | 73.684 |
| (1.3,2.2] | 79 | 0.245 | 24.458 | 317 | 0.981 | 98.142 |
| (2.2,3.1] | 4 | 0.012 | 1.238 | 321 | 0.994 | 99.381 |
| (3.1,4] | 0 | 0.000 | 0.000 | 321 | 0.994 | 99.381 |
| (4,4.9] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (4.9,5.8] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (5.8,6.7] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (6.7,7.6] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (7.6,8.5] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (8.5,9.41] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "Colesterol")
kable(resultados, caption = "Tabla de frecuencia Colesterol")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (19.7,51.7] | 1 | 0.003 | 0.310 | 1 | 0.003 | 0.310 |
| (51.7,83.4] | 1 | 0.003 | 0.310 | 2 | 0.006 | 0.619 |
| (83.4,115] | 12 | 0.037 | 3.715 | 14 | 0.043 | 4.334 |
| (115,147] | 42 | 0.130 | 13.003 | 56 | 0.173 | 17.337 |
| (147,178] | 99 | 0.307 | 30.650 | 155 | 0.480 | 47.988 |
| (178,210] | 103 | 0.319 | 31.889 | 258 | 0.799 | 79.876 |
| (210,242] | 41 | 0.127 | 12.693 | 299 | 0.926 | 92.570 |
| (242,274] | 20 | 0.062 | 6.192 | 319 | 0.988 | 98.762 |
| (274,305] | 2 | 0.006 | 0.619 | 321 | 0.994 | 99.381 |
| (305,337] | 2 | 0.006 | 0.619 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "Triglicéridos")
kable(resultados, caption = "Tabla de frecuencia Triglicéridos")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (6.92,65.8] | 12 | 0.037 | 3.715 | 12 | 0.037 | 3.715 |
| (65.8,124] | 126 | 0.390 | 39.009 | 138 | 0.427 | 42.724 |
| (124,183] | 92 | 0.285 | 28.483 | 230 | 0.712 | 71.207 |
| (183,241] | 50 | 0.155 | 15.480 | 280 | 0.867 | 86.687 |
| (241,299] | 21 | 0.065 | 6.502 | 301 | 0.932 | 93.189 |
| (299,358] | 13 | 0.040 | 4.025 | 314 | 0.972 | 97.214 |
| (358,416] | 2 | 0.006 | 0.619 | 316 | 0.978 | 97.833 |
| (416,474] | 6 | 0.019 | 1.858 | 322 | 0.997 | 99.690 |
| (474,533] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (533,592] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "GOT")
kable(resultados, caption = "Tabla de frecuencia GOT")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (12.3,86.9] | 219 | 0.678 | 67.802 | 219 | 0.678 | 67.802 |
| (86.9,161] | 63 | 0.195 | 19.505 | 282 | 0.873 | 87.307 |
| (161,235] | 24 | 0.074 | 7.430 | 306 | 0.947 | 94.737 |
| (235,309] | 5 | 0.015 | 1.548 | 311 | 0.963 | 96.285 |
| (309,382] | 6 | 0.019 | 1.858 | 317 | 0.981 | 98.142 |
| (382,456] | 1 | 0.003 | 0.310 | 318 | 0.985 | 98.452 |
| (456,530] | 2 | 0.006 | 0.619 | 320 | 0.991 | 99.071 |
| (530,604] | 2 | 0.006 | 0.619 | 322 | 0.997 | 99.690 |
| (604,678] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (678,753] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "GPT")
kable(resultados, caption = "Tabla de frecuencia GPT")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (15.2,91] | 176 | 0.545 | 54.489 | 176 | 0.545 | 54.489 |
| (91,166] | 84 | 0.260 | 26.006 | 260 | 0.805 | 80.495 |
| (166,241] | 24 | 0.074 | 7.430 | 284 | 0.879 | 87.926 |
| (241,316] | 18 | 0.056 | 5.573 | 302 | 0.935 | 93.498 |
| (316,391] | 9 | 0.028 | 2.786 | 311 | 0.963 | 96.285 |
| (391,466] | 5 | 0.015 | 1.548 | 316 | 0.978 | 97.833 |
| (466,541] | 2 | 0.006 | 0.619 | 318 | 0.985 | 98.452 |
| (541,616] | 4 | 0.012 | 1.238 | 322 | 0.997 | 99.690 |
| (616,691] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (691,767] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "GGT")
kable(resultados, caption = "Tabla de frecuencia GGT")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (10.1,199] | 280 | 0.867 | 86.687 | 280 | 0.867 | 86.687 |
| (199,386] | 29 | 0.090 | 8.978 | 309 | 0.957 | 95.666 |
| (386,574] | 8 | 0.025 | 2.477 | 317 | 0.981 | 98.142 |
| (574,761] | 4 | 0.012 | 1.238 | 321 | 0.994 | 99.381 |
| (761,948] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (948,1.14e+03] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (1.14e+03,1.32e+03] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (1.32e+03,1.51e+03] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (1.51e+03,1.7e+03] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (1.7e+03,1.89e+03] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "Fosfatasa.Alcalina")
kable(resultados, caption = "Tabla de frecuencia Fosfatasa Alcalina")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (26.5,73.5] | 94 | 0.291 | 29.102 | 94 | 0.291 | 29.102 |
| (73.5,120] | 142 | 0.440 | 43.963 | 236 | 0.731 | 73.065 |
| (120,166] | 57 | 0.176 | 17.647 | 293 | 0.907 | 90.712 |
| (166,213] | 16 | 0.050 | 4.954 | 309 | 0.957 | 95.666 |
| (213,260] | 7 | 0.022 | 2.167 | 316 | 0.978 | 97.833 |
| (260,306] | 3 | 0.009 | 0.929 | 319 | 0.988 | 98.762 |
| (306,352] | 2 | 0.006 | 0.619 | 321 | 0.994 | 99.381 |
| (352,399] | 0 | 0.000 | 0.000 | 321 | 0.994 | 99.381 |
| (399,446] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (446,492] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "bilirrubina.total")
kable(resultados, caption = "Tabla de frecuencia Bilirrubina total")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (0.222,1.04] | 184 | 0.570 | 56.966 | 184 | 0.570 | 56.966 |
| (1.04,1.84] | 109 | 0.337 | 33.746 | 293 | 0.907 | 90.712 |
| (1.84,2.65] | 18 | 0.056 | 5.573 | 311 | 0.963 | 96.285 |
| (2.65,3.46] | 6 | 0.019 | 1.858 | 317 | 0.981 | 98.142 |
| (3.46,4.27] | 3 | 0.009 | 0.929 | 320 | 0.991 | 99.071 |
| (4.27,5.07] | 0 | 0.000 | 0.000 | 320 | 0.991 | 99.071 |
| (5.07,5.88] | 0 | 0.000 | 0.000 | 320 | 0.991 | 99.071 |
| (5.88,6.69] | 1 | 0.003 | 0.310 | 321 | 0.994 | 99.381 |
| (6.69,7.49] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (7.49,8.31] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "proteínas.totales")
kable(resultados, caption = "Tabla de frecuencia Proteínas totales")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (4.6,5.05] | 1 | 0.003 | 0.310 | 1 | 0.003 | 0.310 |
| (5.05,5.5] | 3 | 0.009 | 0.929 | 4 | 0.012 | 1.238 |
| (5.5,5.95] | 3 | 0.009 | 0.929 | 7 | 0.022 | 2.167 |
| (5.95,6.4] | 17 | 0.053 | 5.263 | 24 | 0.074 | 7.430 |
| (6.4,6.85] | 77 | 0.238 | 23.839 | 101 | 0.313 | 31.269 |
| (6.85,7.3] | 108 | 0.334 | 33.437 | 209 | 0.647 | 64.706 |
| (7.3,7.75] | 66 | 0.204 | 20.433 | 275 | 0.851 | 85.139 |
| (7.75,8.2] | 36 | 0.111 | 11.146 | 311 | 0.963 | 96.285 |
| (8.2,8.65] | 10 | 0.031 | 3.096 | 321 | 0.994 | 99.381 |
| (8.65,9.1] | 2 | 0.006 | 0.619 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "albúmina")
kable(resultados, caption = "Tabla de frecuencia Albúmina")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (2.59,3.11] | 6 | 0.019 | 1.858 | 6 | 0.019 | 1.858 |
| (3.11,3.62] | 20 | 0.062 | 6.192 | 26 | 0.080 | 8.050 |
| (3.62,4.13] | 93 | 0.288 | 28.793 | 119 | 0.368 | 36.842 |
| (4.13,4.64] | 163 | 0.505 | 50.464 | 282 | 0.873 | 87.307 |
| (4.64,5.15] | 39 | 0.121 | 12.074 | 321 | 0.994 | 99.381 |
| (5.15,5.66] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (5.66,6.17] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (6.17,6.68] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (6.68,7.19] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (7.19,7.71] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "sodio")
kable(resultados, caption = "Tabla de frecuencia Sodio")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (128,130] | 2 | 0.006 | 0.619 | 2 | 0.006 | 0.619 |
| (130,132] | 3 | 0.009 | 0.929 | 5 | 0.015 | 1.548 |
| (132,135] | 4 | 0.012 | 1.238 | 9 | 0.028 | 2.786 |
| (135,137] | 30 | 0.093 | 9.288 | 39 | 0.121 | 12.074 |
| (137,139] | 99 | 0.307 | 30.650 | 138 | 0.427 | 42.724 |
| (139,141] | 108 | 0.334 | 33.437 | 246 | 0.762 | 76.161 |
| (141,143] | 43 | 0.133 | 13.313 | 289 | 0.895 | 89.474 |
| (143,146] | 32 | 0.099 | 9.907 | 321 | 0.994 | 99.381 |
| (146,148] | 1 | 0.003 | 0.310 | 322 | 0.997 | 99.690 |
| (148,150] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "potasio")
kable(resultados, caption = "Tabla de frecuencia Potasio")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (3.4,3.66] | 4 | 0.012 | 1.238 | 4 | 0.012 | 1.238 |
| (3.66,3.92] | 21 | 0.065 | 6.502 | 25 | 0.077 | 7.740 |
| (3.92,4.18] | 33 | 0.102 | 10.217 | 58 | 0.180 | 17.957 |
| (4.18,4.44] | 85 | 0.263 | 26.316 | 143 | 0.443 | 44.272 |
| (4.44,4.7] | 89 | 0.276 | 27.554 | 232 | 0.718 | 71.827 |
| (4.7,4.96] | 42 | 0.130 | 13.003 | 274 | 0.848 | 84.830 |
| (4.96,5.22] | 27 | 0.084 | 8.359 | 301 | 0.932 | 93.189 |
| (5.22,5.48] | 8 | 0.025 | 2.477 | 309 | 0.957 | 95.666 |
| (5.48,5.74] | 8 | 0.025 | 2.477 | 317 | 0.981 | 98.142 |
| (5.74,6] | 6 | 0.019 | 1.858 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "leucocitos")
kable(resultados, caption = "Tabla de frecuencia Leucocitos")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (1.69e+03,2.79e+03] | 19 | 0.059 | 5.882 | 19 | 0.059 | 5.882 |
| (2.79e+03,3.89e+03] | 53 | 0.164 | 16.409 | 72 | 0.223 | 22.291 |
| (3.89e+03,4.98e+03] | 65 | 0.201 | 20.124 | 137 | 0.424 | 42.415 |
| (4.98e+03,6.08e+03] | 65 | 0.201 | 20.124 | 202 | 0.625 | 62.539 |
| (6.08e+03,7.17e+03] | 54 | 0.167 | 16.718 | 256 | 0.793 | 79.257 |
| (7.17e+03,8.26e+03] | 39 | 0.121 | 12.074 | 295 | 0.913 | 91.331 |
| (8.26e+03,9.36e+03] | 14 | 0.043 | 4.334 | 309 | 0.957 | 95.666 |
| (9.36e+03,1.05e+04] | 7 | 0.022 | 2.167 | 316 | 0.978 | 97.833 |
| (1.05e+04,1.15e+04] | 4 | 0.012 | 1.238 | 320 | 0.991 | 99.071 |
| (1.15e+04,1.27e+04] | 3 | 0.009 | 0.929 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "hematocrito")
kable(resultados, caption = "Tabla de frecuencia Hematocrito")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (24,27.3] | 1 | 0.003 | 0.310 | 1 | 0.003 | 0.310 |
| (27.3,30.6] | 3 | 0.009 | 0.929 | 4 | 0.012 | 1.238 |
| (30.6,33.9] | 8 | 0.025 | 2.477 | 12 | 0.037 | 3.715 |
| (33.9,37.2] | 49 | 0.152 | 15.170 | 61 | 0.189 | 18.885 |
| (37.2,40.5] | 82 | 0.254 | 25.387 | 143 | 0.443 | 44.272 |
| (40.5,43.8] | 78 | 0.241 | 24.149 | 221 | 0.684 | 68.421 |
| (43.8,47.1] | 72 | 0.223 | 22.291 | 293 | 0.907 | 90.712 |
| (47.1,50.4] | 23 | 0.071 | 7.121 | 316 | 0.978 | 97.833 |
| (50.4,53.7] | 4 | 0.012 | 1.238 | 320 | 0.991 | 99.071 |
| (53.7,57] | 3 | 0.009 | 0.929 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "plaquetas")
kable(resultados, caption = "Tabla de frecuencia Plaquetas")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (9.71e+03,3.87e+04] | 3 | 0.009 | 0.929 | 3 | 0.009 | 0.929 |
| (3.87e+04,6.74e+04] | 12 | 0.037 | 3.715 | 15 | 0.046 | 4.644 |
| (6.74e+04,9.61e+04] | 35 | 0.108 | 10.836 | 50 | 0.155 | 15.480 |
| (9.61e+04,1.25e+05] | 80 | 0.248 | 24.768 | 130 | 0.402 | 40.248 |
| (1.25e+05,1.54e+05] | 69 | 0.214 | 21.362 | 199 | 0.616 | 61.610 |
| (1.54e+05,1.82e+05] | 68 | 0.211 | 21.053 | 267 | 0.827 | 82.663 |
| (1.82e+05,2.11e+05] | 31 | 0.096 | 9.598 | 298 | 0.923 | 92.260 |
| (2.11e+05,2.4e+05] | 15 | 0.046 | 4.644 | 313 | 0.969 | 96.904 |
| (2.4e+05,2.68e+05] | 9 | 0.028 | 2.786 | 322 | 0.997 | 99.690 |
| (2.68e+05,2.97e+05] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "Índice.de.Quick")
kable(resultados, caption = "Tabla de frecuencia Índice de Quick")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (44.9,52.3] | 1 | 0.003 | 0.310 | 1 | 0.003 | 0.310 |
| (52.3,59.6] | 4 | 0.012 | 1.238 | 5 | 0.015 | 1.548 |
| (59.6,66.9] | 3 | 0.009 | 0.929 | 8 | 0.025 | 2.477 |
| (66.9,74.2] | 4 | 0.012 | 1.238 | 12 | 0.037 | 3.715 |
| (74.2,81.5] | 13 | 0.040 | 4.025 | 25 | 0.077 | 7.740 |
| (81.5,88.8] | 18 | 0.056 | 5.573 | 43 | 0.133 | 13.313 |
| (88.8,96.1] | 39 | 0.121 | 12.074 | 82 | 0.254 | 25.387 |
| (96.1,103] | 240 | 0.743 | 74.303 | 322 | 0.997 | 99.690 |
| (103,111] | 0 | 0.000 | 0.000 | 322 | 0.997 | 99.690 |
| (111,118] | 1 | 0.003 | 0.310 | 323 | 1.000 | 100.000 |
resultados <- Tabla.frec(datos, "fibrinógeno")
kable(resultados, caption = "Tabla de frecuencia Fibrinógeno")| Var | Freq | Rel | Rel.per | FreqAc | RelAc | Freq.PerAc |
|---|---|---|---|---|---|---|
| (123,172] | 13 | 0.040 | 4.037 | 13 | 0.040 | 4.037 |
| (172,220] | 48 | 0.149 | 14.907 | 61 | 0.189 | 18.944 |
| (220,269] | 98 | 0.304 | 30.435 | 159 | 0.494 | 49.379 |
| (269,318] | 90 | 0.280 | 27.950 | 249 | 0.773 | 77.329 |
| (318,366] | 47 | 0.146 | 14.596 | 296 | 0.919 | 91.925 |
| (366,415] | 15 | 0.047 | 4.658 | 311 | 0.966 | 96.584 |
| (415,464] | 7 | 0.022 | 2.174 | 318 | 0.988 | 98.758 |
| (464,513] | 1 | 0.003 | 0.311 | 319 | 0.991 | 99.068 |
| (513,561] | 2 | 0.006 | 0.621 | 321 | 0.997 | 99.689 |
| (561,610] | 1 | 0.003 | 0.311 | 322 | 1.000 | 100.000 |